Core Insights - Zhihua Technology announced a strategic investment of 1 billion yuan from Shanghai Pudong Innovation Investment Development Group and Zhangjiang Group to enhance its AI infrastructure [1] - The company unveiled its new visual language model GLM-4.1V-Thinking, which excels in multimodal reasoning tasks, marking a significant advancement in AI capabilities [2][3] - The lightweight version, GLM-4.1V-9B-Thinking, achieved top performance in 23 out of 28 authoritative evaluations, demonstrating the potential of smaller models [3] Investment and Collaboration - The strategic investment aims to foster deeper collaboration in equity investment, industrial empowerment, and ecosystem development between Zhihua Technology and its partners [2] - The partnership with Zhangjiang Group includes the establishment of the "Moli Community," which focuses on large model enterprises and provides a dedicated model pool for member companies [1] Technological Advancements - GLM-4.1V-Thinking supports various input types, including images, videos, and documents, and is designed for complex cognitive tasks, such as live sports commentary [2] - The model incorporates a "Chain-of-Thought Reasoning" mechanism and a "curriculum sampling reinforcement learning strategy" to enhance cross-modal causal reasoning capabilities [2] New Platforms and Initiatives - The launch of the "Agent Application Space" aims to aggregate AI agent capabilities for enterprise clients and developers, facilitating easy access to advanced AI functionalities [3] - The "Agents Pioneer Program" will invest hundreds of millions to support AI agent startups, promoting innovation in the AI ecosystem [3] Industry Perspective - The future of AI is seen as a reconstruction of production paradigms, emphasizing the role of developers, designers, and entrepreneurs as co-creators of the intelligent ecosystem [4]

Imagine you've built a perfect agent for your blog writing team. Now your social media team wants to use it but they need different prompts, different models and different tools. But modifying your underlying code for each use case is not only time consuming but also prone to errors.This creates two distinct problems. Developers get stuck in constant code changing cycles that slow down iteration while business teams can't experiment without engineering support. That's where Lang graph assistants come in.Tod ...

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Welcome everyone. Uh my name is Mason Edgar. I work at Temporal and today we're going to talk about uh events are the wrong abstraction for your AI agents.So uh who here raise of hands recognizes what this diagram is out of curiosity. Okay. So this is a map of our solar system um in a geocentric projection. Uh this is where we have earth as the center of our solar system and this is how celestial objects move around the earth.And this was used to kind of calculate uh celestial trajectories prior to like the ...

Finally! You can reliably test AI Agents without humans:- One Agent asks questions.- The Agent being tested responds.- Another Agent Judges.Here’s a complete breakdown (with code): https://t.co/wDm2a4zN0bAvi Chawla (@_avichawla):How Agents test Agents, clearly explained (with code): ...

Agents are all the rage in 2025, and every single b2b SaaS startup/incumbent promises AI agents that can "automate work" in some way. But how do you actually build this? The answer is two fold: 1. really really good tools 2. carefully tailored agent reasoning over these tools that range from assistant-to-automation based UXs. The main goal of this talk is to a practical overview of agent architectures that can automate real-world work, with a focus on document-centric tasks. Learn the core building blocks o ...

10 real-world projects for AI engineers that cover:- Memory- Video RAG- Voice Agent- Corrective RAG- and much more!Find them in the explainer thread below. https://t.co/RucVrx4Xt0Avi Chawla (@_avichawla):AI Engineering Hub is about to cross 10k GitHub stars!It’s 100% open-source and hosts 70+ free hands-on demos.Here are 10 MCP, RAG, and AI Agents projects for AI engineers: https://t.co/Zxe2K6DEKg ...

Financial Data and Key Metrics Changes - In Q1 2025, total revenue reached $259 million, up 36% year over year and 9% quarter over quarter [14] - Gross profit increased by 67% year over year to $190 million, with a gross margin of 73.2%, up from 59.2% a year ago [14] - Operating loss narrowed to $27 million from $81 million in the year-ago quarter [15] - Net loss attributable to shareholders was $33 million, reduced from $80 million in the year-ago quarter [15] Business Line Data and Key Metrics Changes - The Internet business saw a 46% increase in revenue year over year, with an operating margin nearly doubling to 15.5% [5][16] - Losses from the AI and Other segment narrowed to $46 million compared to $82 million a year ago [16] Market Data and Key Metrics Changes - The company is focusing on scalable, modernizable use cases in AI and robotics, leveraging open-source models to enhance performance [17] - The total headcount was approximately 850, down from 862 a year ago, indicating cost control measures [18] Company Strategy and Development Direction - The company aims to strengthen its position in both traditional and new business areas, with a focus on AI and robotics [4] - AI is central to the company's strategy, with significant investments in R&D and product upgrades [8] - The company plans to enhance its legacy Internet business while pushing forward with AI initiatives [11] Management's Comments on Operating Environment and Future Outlook - Management expressed optimism about the ongoing positive momentum in revenue growth and loss reduction [5] - The company is cautious about the robotics industry's commercialization timeline, indicating it may take over five years for humanoid robots to achieve significant market presence [52] Other Important Information - The company has over $200 million in cash, providing strategic flexibility for potential acquisitions [66] - The focus is on transforming traditional software into AI-driven applications, emphasizing user-centered payment models [45][46] Q&A Session Summary Question: Will Cheetah's future development focus more on AI tools or robots? - Management believes both areas are complementary, with a short-term focus on AI tools due to rapid development potential [22][24] Question: What are the company's thoughts on data asset construction in robotics? - Management is cautious about the current state of data conversion in robotics and does not plan to provide external data services at this time [26][30] Question: How does the company balance open-source models and self-developed approaches? - The company prioritizes efficiency and will use open-source models when they provide better results [32][33] Question: What is the company's commercialization path for AI tools? - The company is adopting a subscription model for AI tools, which has shown user acceptance and willingness to pay [42][44] Question: Will the company achieve overall break-even in the second half of 2025? - Achieving profitability is a major goal, but it depends on core business progress and market conditions [71][72]

Core Idea - Factory believes software development is transitioning to agent-driven from human-driven [1] - To achieve significant productivity gains (5-20x), a shift from collaborating with AI to delegating tasks entirely to AI is needed [3] - Factory is building a platform for managing and scaling AI agents, integrating various engineering systems [3][4][5] Agentic System Characteristics - Agentic systems require planning to decide future actions [11] - Decision-making is crucial for agents to make calls based on the existing state [13][14] - Environmental grounding is necessary for agents to interact with and adapt to the external environment [14] Human-AI Collaboration - Humans will remain in software development, focusing on the outer loop (reasoning, requirements) [15][16] - Agents will handle the inner loop (coding, testing, code review) [17] - AI UX should blend delegation with control for situations where agents cannot complete tasks [17] Agent Reliability - Clear planning and boundaries are essential for reliable agents [32] - Subtask decomposition, model predictive control, and explicit plan templating can improve planning [19][20] - Control over the tools agents use is the most important differentiator in agent reliability [28] Environmental Interaction - New AI computer interfaces are needed for agents to interact with the world [28] - Processing information from the environment is crucial for complex systems [29][30] - Agents need to ground themselves in the environment to perform full software development work [32] Call to Action - Factory encourages teams not delegating at least 50% of engineering tasks to AI agents to engage with them [34]

With the rise of MCP servers, A2A, and our trusty friend, OpenAPI, it turns out the web browser may be the default MCP server for the rest of the internet. In this talk, we'll walk through how a web browsing tool is probably the only tool you'll need to enable production AI Agents. About Paul Klein IV Paul Klein IV is a San‑Francisco‑based serial entrepreneur and engineer. After honing his chops at Twilio during it's IPO and founding Stream Club—a live‑streaming platform acquired by Mux in 2021 he launched ...